class_docstr: Layer that processes dropout with exponential decayed keep rate during
  training.
  
  :param initial_keep_rate: the initial keep rate of decaying dropout.
  :param decay_interval: the decay interval of decaying dropout.
  :param decay_rate: the decay rate of decaying dropout.
  :param noise_shape: a 1D integer tensor representing the shape of the
      binary dropout mask that will be multiplied with the input.
  :param seed: a python integer to use as random seed.
  :param kwargs: standard layer keyword arguments.
  
  Examples:
      >>> import matchzoo as mz
      >>> layer = mz.contrib.layers.DecayingDropoutLayer(
      ...     initial_keep_rate=1.0,
      ...     decay_interval=10000,
      ...     decay_rate=0.977,
      ... )
      >>> num_batch, num_dim =5, 10
      >>> layer.build([num_batch, num_dim])
docstr: :class: 'DecayingDropoutLayer' constructor.
docstr_tok: [":","class",":","'DecayingDropoutLayer","'","constructor","."]
code: def __init__(
      self,
      initial_keep_rate: float = 1.0,
      decay_interval: int = 10000,
      decay_rate: float = 0.977,
      noise_shape=None,
      seed=None,
      **kwargs,
  ):
      """:class: 'DecayingDropoutLayer' constructor."""
      super(DecayingDropoutLayer, self).__init__(**kwargs)
      self._iterations = None
      self._initial_keep_rate = initial_keep_rate
      self._decay_interval = decay_interval
      self._decay_rate = min(1.0, max(0.0, decay_rate))
      self._noise_shape = noise_shape
      self._seed = seed
  
file_path: MatchZoo/matchzoo/contrib/layers/decaying_dropout_layer.py
imports: import tensorflow
  from keras import backend
  from keras.engine import Layer
label_desc: Use image node_id 1 to create a new DecayingDropoutLayer object from inherited base classes: Layer with example: obj = DecayingDropoutLayer(initial_keep_rate, decay_interval, decay_rate, noise_shape, seed)